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This article presents an online parameter identification scheme for advection-diffusion processes using data collected by a mobile sensor network. The advection-diffusion equation is incorporated into the information dynamics associated with the trajectories of the mobile sensors. A constrained cooperative Kalman filter is developed to provide estimates of the field values and gradients along the trajectories of the mobile sensors so that the temporal variations in the field values can be estimated. This leads to a co-design scheme for state estimation and parameter identification for advection-diffusion processes that is different from comparable schemes using sensors installed at fixed spatial locations. Using state estimates from the constrained cooperative Kalman filter, a recursive least-square (RLS) algorithm is designed to estimate unknown model parameters of the advection-diffusion processes. Theoretical justifications are provided for the convergence of the proposed cooperative Kalman filter by deriving a set of sufficient conditions regarding the formation shape and the motion of the mobile sensor network. Simulation and experimental results show satisfactory performance and demonstrate the robustness of the algorithm under realistic uncertainties and disturbances.
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Incorporating impact of hazardous and toxic environments on the guidance of mobile sensor networks used for the cooperative estimation of spatially distributed processes
This work incorporates the effects that hazardous environments have on sensing devices, in the guidance of mobile platforms with onboard sensors. Mobile sensors are utilized in the state reconstruction of spatiotemporally varying processes, often described by advection-diffusion PDEs. A typical sensor guidance policy is based on a gradient ascent scheme which repositions the sensors to spatial regions that have larger state estimation errors. If the cumulative measurements of the spatial process are used as a means to represent the effects of hazardous environments on the sensors, then the sensors are considered inoperable the instance the cumulative measurements exceed a device-specific tolerance level. A binary guidance policy considered earlier repositioned the sensors to regions of larger values of the state estimation errors thus implementing an information-sensitive policy. The policy switched to an information-averse guidance the instance the cumulative effects exceeded a certain tolerance level. Such a binary policy switches the sensor velocity abruptly from a positive to a negative value. To alleviate these discontinuity effects, a ternary guidance policy is considered and which inserts a third guidance policy, the information-neutral policy, that smooths out the transitions from information-sensitive to information-averse guidance. A novelty in this ternary guidance has to do with the level-set approach which changes from a guidance towards large values of the state estimation error towards level sets of the state estimation error and eventually towards reduced values of the state estimation error. An example on an advection-diffusion PDE in 2D employing a single interior mobile sensor using both the binary and ternary guidance policies is used to demonstrate the effects of hazardous environments on both the sensor life expectancy and the performance of the state estimator.
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- Award ID(s):
- 1825546
- PAR ID:
- 10110160
- Date Published:
- Journal Name:
- 2018 IEEE Conference on Decision and Control (CDC)
- Page Range / eLocation ID:
- 1317 to 1322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CDC.2018.8619588
